Dynamic POI Group Recommendation Based on Multi-dimensional User Preference Model

doi:10.3778/j.issn.1673-9418.2207107

Abstract

Abstract: With the massive quantification of networked data and the development of geo-social networks (GSNs), group activities are prevalent in people’s life. The objects of recommendation systems are extended from individ-uals to user groups. Point-of-interest (POI) group recommendation problem is also gradually known as a hot research topic. However, the traditional methods are not suitable for group recommendation in geographic social networks, due to the multifactorial influence of user preferences in GSNs and the complexity of the group decision-making process. To reveal user preferences and the effect of the group decision process on group recommendation, this paper proposes a neural network-based model for dynamic POI group recommendation by leveraging multi-dimensional user preference. Firstly, the proposed model combines temporal and spatial factors to calculate user preferences based on user behavior activity records and builds a group-point-of-interest perception graph with group as unit. Next, this paper adds the influence of collaborative users to model group preferences, which fully considers the characteristics of GSNs, to ensure the accuracy of POI group recommendation. Finally, a neural network-based model can be constructed to simulate group decision-making, which can ensure the accuracy of POI recommen-dations. This paper conducts extensive experiments by comparing the existing group recommendation algorithms on the real datasets to demonstrate the performance of the method proposed in this paper. Experimental results show that the proposed method is significantly better than the existing algorithms in terms of the hit rate of POI, which proves the effectiveness of the proposed algorithm.

Key words: point-of-interest group recommendation, neural network structure, geo-social networks

摘要： 随着互联网数据信息的海量化和地理社交网络（GSNs）的不断发展，群组活动在社会生活中盛行，推荐问题的对象由个人向群组进行延伸，兴趣点（POI）群组推荐问题也逐渐成为研究的热点问题。由于GSNs中用户偏好的多因素影响和群组决策过程的复杂化，传统的方法已经不再适用。为了充分挖掘GSNs中用户偏好和模拟群组决策过程，以提高群组推荐的整体性能，提出了一种基于多维度偏好建模的动态兴趣点群组推荐方法。首先，结合时间因素和空间因素，根据用户行为活动记录计算用户偏好，并以群组为单位构建群组-兴趣点感知图；然后，加入协同用户的影响完成对用户群组偏好的建模，并充分考虑了GSNs中的特征，保证了兴趣点推荐的准确性；最后，利用神经网络结构模拟群组决策过程，完成对兴趣点群组推荐任务的求解。在真实数据集上与现有的群组推荐算法进行了对比，实验结果表明提出的算法在兴趣点命中率等方面明显优于对照算法，证明了该算法的有效性。

关键词: 兴趣点群组推荐, 神经网络结构, 地理社交网络

SUN Mingyang, MA Yuliang, YUAN Ye, WANG Guoren. Dynamic POI Group Recommendation Based on Multi-dimensional User Preference Model[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(10): 2478-2487.

孙明阳, 马玉亮, 袁野, 王国仁. 多维度偏好建模的动态兴趣点群组推荐算法[J]. 计算机科学与探索, 2023, 17(10): 2478-2487.

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